mlsanityData to 7 Oct 2026

Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search

arXiv 2004.08546 ↗First posted 18 Apr 2020cs.LG cs.CV cs.DCcited by 30

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Abstract (arXiv)

Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions. When training deep learning models under an FL setting, people employ the predefined model architecture discovered in the centralized environment. However, this predefined architecture may not be the optimal choice because it may not fit data with non-identical and independent distribution (non-IID). Thus, we advocate automating federated learning (AutoFL) to improve model accuracy and reduce the manual design effort. We specifically study AutoFL via Neural Architecture Search (NAS), which can automate the design process. We propose a Federated NAS (FedNAS) algorithm to help scattered workers collaboratively searching for a better architecture with higher accuracy. We also build a system based on FedNAS. Our experiments on non-IID dataset show that the architecture searched by FedNAS can outperform the manually predefined architecture.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2020.

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Citing papers per year: 2021: 13; 2022: 8; 2023: 3; 2024: 3; 2025: 0; 2026: 3.

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  1. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  2. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  3. 2021A Field Guide to Federated Optimizationcited by 312
  4. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  5. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  6. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  7. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  8. 2021KNAS: Green Neural Architecture Searchcited by 33
  9. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  10. 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
  11. 2022Federated Learning Challenges and Opportunities: An Outlookcited by 19
  12. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
18 more
  1. 2021SPIDER: Searching Personalized Neural Architecture for Federated Learningcited by 13
  2. 2024FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneitycited by 13
  3. 2021Self-supervised Cross-silo Federated Neural Architecture Searchcited by 11
  4. 2021FedMe: Federated Learning via Model Exchangecited by 10
  5. 2021FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networkscited by 5
  6. 2021AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruningcited by 4
  7. 2022Adaptive Expert Models for Personalization in Federated Learningcited by 4
  8. 2022FedorAS: Federated Architecture Search under system heterogeneitycited by 4
  9. 2022Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Searchcited by 3
  10. 2022FEATHERS: Federated Architecture and Hyperparameter Searchcited by 3
  11. 2022FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Trainingcited by 3
  12. 2023A Framework for Incentivized Collaborative Learningcited by 2
  13. 2024DynamicFL: Federated Learning with Dynamic Communication Resource Allocationcited by 1
  14. 2026DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Trainingcited by 1
  15. 2022FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferencescited by 0
  16. 2024Fair Differentiable Neural Network Architecture Search for Long-Tailed Data with Self-Supervised Learningcited by 0
  17. 2026Optimized Federated Knowledge Distillation with Distributed Neural Architecture Searchcited by 0
  18. 2026FEAST: Federated Shared-Space Training for Resource-Heterogeneous Clientscited by 0